A Holistic Algorithm for Materials Requirement Planning in Collaborative Networks

[EN] Collaboration has increasingly been considered a key topic within the small and medium-sized enterprises, allowing dealing with the intense competitiveness of today¿s globalised markets. The European H2020 Cloud Collaborative Manufacturing Networks Project proposes mechanisms to encourage collaboration among enterprises, through the computation of collaborative plans. Particularly, this paper focuses on the proposal of a holistic algorithm to deal with the automated and collaborative calculation of the Materials Requirement Plan. The proposed algorithm is validated in a collaborative network belonging to the automotive industry.The research leading to these results is in the frame of the “Cloud Collaborative Manufacturing Networks” (C2NET) project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 636909.Andres, B.; Poler, R.; Sanchis, R. (2017). A Holistic Algorithm for Materials Requirement Planning in Collaborative Networks. IFIP Advances in Information and Communication Technology. 560:41-50. https://doi.org/10.1007/978-3-319-65151-4_4S4150560CORDIS Europa: Factories of the Future. H2020-EU.2.1.5.1. - Technologies for Factories of the Future (2014)H2020 Project C2NET (2015). http://cordis.europa.eu/project/rcn/193440_en.htmlAndres, B., Sanchis, R., Poler, R.: A cloud platform to support collaboration in supply networks. Int. J. Prod. Manag. Eng. 4(1), 5–13 (2016)Andres, B., Sanchis, R., Lamothe, J., Saari, L., Hauser, F.: Integrated production-distribution planning optimization models: a review in collaborative networks context. Int. J. Prod. Manag. Eng. 5(1), 31–38 (2017)Camarinha-Matos, L.M., Afsarmanesh, H.: Collaborative networks: a new scientific discipline. J. Intell. Manuf. 16(4–5), 439–452 (2005)Andres, B., Poler, R.: Models, guidelines and tools for the integration of collaborative processes in non-hierarchical manufacturing networks: a review. Int. J. Comput. Integr. Manuf. 2(29), 166–201 (2016)Sanchis, R., Poler, R., Lario, F.C.: Identification and analysis of Disruptions: the first step to understand and measure Enterprise Resilience. In: International Conference on Industrial Engineering and Engineering Management, pp. 424–431 (2012)Andres, B., Saari, L., Lauras, M., Eizaguirre, F.: Optimization algorithms for collaborative manufacturing and logistics processes. In: Zelm, M., Doumeingts, G., Mendonça, J.P. (eds.) Enterprise Interoperability in the Digitized and Netwroked Factory of the Future, iSTE 2016, pp. 167–173 (2016)Orbegozo, A., Andres, B., Mula, J., Lauras, M., Monteiro, C., Malheiro, M.: An overview of optimization models for integrated replenishment and production planning decisions. In: Building Bridges Between Researchers and Practitioners. Book of Abstracts of the International Joint Conference CIO-ICIEOM-IISE-AIM (IJC 2016), p. 68 (2016

A proposal of performance indicators for collaborative business ecosystems

Business ecosystems enabled by the increasing use and improvement of communication networks, offer nowadays a powerful competitive advantage to business players and entrepreneurs. They form a collaborative new mean of economic and social value creation, addressing customers' needs, overcoming constraints of individual firms, increasing capabilities for new business opportunities, and accelerating learning and innovation. This paper proposes a set of performance indicators to measure some of these collaboration benefits, therefore motivating the sustainability and resilience of the business ecosystem. The presented results are based on simulation models, which intend to characterize the roles and interactions of a real life collaborative business ecosystem.info:eu-repo/semantics/publishedVersio

A Modeling Framework to Assess Strategies Alignment based on Collaborative Network Emotions

[DE] The Collaborative Networks (CN) discipline has been largely studied in last decades, addressing different problems and proposing solutions for the robust establishment of collaborative processes, within the enterprises willing to collaborate. The main aim of CN research is, therefore, to generate approaches that enable creating effective relationships in the long term, to achieve stable and agile alliances. The concept of alignment among the CN partners has been considered since the beginning of CN research. Nevertheless, novel perspectives of study in CN, such as the consideration of collaborative emotional states, within the CN, have been introduced in recent years. This paper connects the research area of strategies alignment and the CN emotion models. Accordingly, a modelling framework to assess strategies alignment considering the emotional environment within the CN is proposed. The modelling framework allows representing how the enterprises emotions affect in the selection and alignment of formulated enterprises¿ strategiesAndres, B.; Ferrada, F.; Poler, R.; Camarinha-Matos, L. (2018). A Modeling Framework to Assess Strategies Alignment based on Collaborative Network Emotions. IFIP Advances in Information and Communication Technology. 534:349-361. https://doi.org/10.1007/978-3-319-99127-6_30S349361534Camarinha-Matos, L.M.: Collaborative networks in industry and the role of PRO-VE. Int. J. Prod. Manag. Eng. 2(2), 53–57 (2014)Andres, B., Poler, R.: Models, guidelines and tools for the integration of collaborative processes in non-hierarchical manufacturing networks: a review. Int. J. Comput. Integr. Manuf. 2(29), 166–201 (2016)Bititci, U., Martinez, V., Albores, P., Parung, J.: Creating and managing value in collaborative networks. Int. J. Phys. Distrib. Logist. Manag. 34(3/4), 251–268 (2004)Carbo, B.: Align the organization for improved supply chain performance. ASCET Proj. 2, 244–447 (2002)Macedo, P., Camarinha-Matos, L.: Value systems alignment analysis in collaborative networked organizations management. Appl. Sci. 7(12), 123 (2017)Andres, B., Poler, R.: A decision support system for the collaborative selection of strategies in enterprise networks. Decis. Support Syst. 91, 113–123 (2016)Andres, B., Macedo, P., Camarinha-Matos, L.M., Poler, R.: Achieving coherence between strategies and value systems in collaborative networks. In: Camarinha-Matos, L.M., Afsarmanesh, H. (eds.) PRO-VE 2014. IFIP AICT, vol. 434, pp. 261–272. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44745-1_26Ferrada, F., Camarinha-Matos, L.M.: A system dynamics and agent-based approach to model emotions in collaborative networks. In: Camarinha-Matos, L.M., Parreira-Rocha, M., Ramezani, J. (eds.) DoCEIS 2017. IFIP AICT, vol. 499, pp. 29–43. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56077-9_3Campuzano, F., Mula, J.: Supply Chain Simulation. A System Dynamics Approach for Improving Performance. Springer, London (2011). https://doi.org/10.1007/978-0-85729-719-8Camarinha-Matos, L.M., Afsarmanesh, H.: Collaborative networks: a new scientific discipline. J. Intell. Manuf. 16(4–5), 439–452 (2005)Vicsek, T.: Complexity: the bigger picture. Nature 418(6894), 131 (2002)Sterman, J., Richardson, G., Davidsen, P.: Modelling the estimation of petroleum resources in the United States. Technol. Forecast. Soc. Chang. 33(3), 219–249 (1998)Vlachos, D., Georgiadis, P., Iakovou, E.: A system dynamics model for dynamic capacity planning of remanufacturing in closed-loop supply chains. Comput. Oper. Res. 34(2), 367–394 (2007)Campuzano-Bolarín, F., Mula, J., Peidro, D.: An extension to fuzzy estimations and system dynamics for improving supply chains. Int. J. Prod. Res. 51(10), 3156–3166 (2013)Barton, P., Bryan, S., Robinson, S.: Modelling in the economic evaluation of health care: selecting the appropriate approach. J. Heal. Serv. Res. Policy 9(2), 110–118 (2004)Eldabi, T., Paul, R.J., Young, T.: Simulation modelling in healthcare: reviewing legacies and investigating futures. J. Oper. Res. Soc. Spec. Issue Oper. Res. Heal. 58(2), 262–270 (2007)Andres, B., Poler, R., Camarinha-Matos, L.M., Afsarmanesh, H.: A simulation approach to assess partners selected for a collaborative network. Int. J. Simul. Model. 16(3), 399–411 (2017)Gohari, A., Mirchi, A., Madan, K.: System dynamics evaluation of climate change adaptation strategies for water resources management in central Iran. Water Resour. Manag. 31(5), 1413–1434 (2007)Fishera, D., Norvell, J., Sonka, S., Nelson, M.J.: Understanding technology adoption through system dynamics modeling: implications for agribusiness management. Int. Food Agribus. Manag. Rev. 3, 281–296 (2000)Lyneisa, J.M.: System dynamics for market forecasting and structural analysis. Syst. Dyn. Rev. 16(1), 3–25 (2000)Borshchev, A., Filippov, A.: From system dynamics and discrete event to practical agent based modeling: reasons, techniques, tools. In: The 22nd International Conference of the System Dynamics Society (2004)Ferrada, F.: C-EMO: A Modeling Framework for Collaborative Network Emotions Doctoral dissertation, Nova University of Lisbon, Portugal (2017). https://run.unl.pt/handle/10362/26857Scherer, K.R.: Emotions are emergent processes: they require a dynamic computational architecture. Rev. Philos. Trans. R. Soc. Biol. Sci. 364(1535), 3459–3474 (2009

A Decision Support Tool for the Selection of Promoting Actions to Encourage Collaboration in Projects for the Agriculture Sector

[EN] Development and innovation agencies promote consortiums of agricultural stakeholders to collaborate in the proposal of projects for public calls. To achieve this partnerships, these agencies should select between different promoting actions to be performed with two objectives: maximize the number of project proposals presented and minimize the resources invested. To support agencies with these decisions, a computer tool based on a multi-objective integer linear programming model is proposed. To deal with the two objectives the weighting sum method is implemented. The model is validated in different scenarios by means a realistic case of an agency in Brittany (France). The results show the conflict between the two objectives considered and the dependency of the solutions on the scenarios defined. As a conclusion it can be stated that: 1) decision-makers should be careful in defining the weights of each objective and 2) the impact of the different promoting actions on the level of stakeholders¿ participation should be precisely estimated.The authors acknowledge the support of the project 691249, RUCAPS: "Enhancing and implementing knowledge based ICT solutions within high risk and uncertain conditions for agriculture production systems", funded by the European Union¿s research and innovation programme under the H2020 Marie Sk¿odowska-Curie Actions.Alemany Díaz, MDM.; Alarcón Valero, F.; Pérez Perales, D.; Guyon, C. (2020). A Decision Support Tool for the Selection of Promoting Actions to Encourage Collaboration in Projects for the Agriculture Sector. IFIP Advances in Information and Communication Technology. 598:534-545. https://doi.org/10.1007/978-3-030-62412-5_44S534545598European Comission Funded Programs. https://ec.europa.eu/programmes/horizon2020Zoie, C., Radulescu, M.: Decision analysis for the project selection problem under risk. IFAC Proc. 34(8), 445–450 (2001)Sadi-Nezhad, S.: A state-of-art survey on project selection using MCDM techniques. J. Project Manage. 2, 1–10 (2017)Caballero, H.C., Chopra, S., Schmidt, E.K.: Project portfolio selection using mathematical programming and optimization methods. In: Paper presented at PMI® Global Congress 2012–North America, Vancouver, British Columbia, Canada, Newtown Square, PA, Project Management Institute (2012)Ahmad, B., Haq, I.: Project selection techniques, relevance and applications in Pakistan. Int. J. Technol. Res. 4, 52–60 (2016)Inuiguchi, M., Ramı́k, J.: Possibilistic linear programming: a brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy Sets Syst. 111(1), 3–28 (2000)Stewart, R., Mohamed, S.: IT/IS projects selection using multi-criteria utility theory. Log. Inf. Manage. 15(4), 254–270 (2002)Alzober, W., Yaakub, A.R.: Integrated model for MCDM: selection contractor in Malaysian construction industry. In: Applied Mechanics and Materials 548, pp. 1587–1595. Trans Tech Publications (2014)Adhikary, P., Roy, P.K., Mazumdar, A.: Optimal renewable energy project selection: a multi-criteria optimization technique approach. Global J. Pure Appl. Math. 11(5), 3319–3329 (2015)Strang, K.D.: Portfolio selection methodology for a nuclear project. Project Manage. J. 42(2), 81–93 (2011)Benjamin, C.O.: A linear goal-programming model for public-sector project selection. J. Oper. Res. Soc. 36(1), 13–23 (1985)Coronado, J.R., Pardo-Mora, E.M., Valero, M.: A multi-objective model for selection of projects to finance new enterprise SMEs in Colombia. J. Ind. Eng. Manage. 4(3), 407–417 (2011)Mat, N.A.C., Cheung, Y.: Partner selection: criteria for successful collaborative network. In: 20th Australian Conference on Information Systems, pp. 631–641 (2009)Camarinha-Matos, L.M., Afsarmanesh, H.: Collaborative Networks. In: Wang, K., Kovacs, G.L., Wozny, M., Fang, M. (eds.) PROLAMAT 2006. IIFIP, vol. 207, pp. 26–40. Springer, Boston, MA (2006). https://doi.org/10.1007/0-387-34403-9_4Paixão, M., Sbragia, R., Kruglianskas, I.: Factors for selecting partners in innovation projects–evidences from alliances in the Brazilian petrochemical leader. Rev. Admin. Innov. São Paulo 11(2), 241–272 (2014)Duisters, D., Duysters, G., de Man, A.P.: The partner selection process: steps, effectiveness, governance. Ann. Hematol. 2, 7–25 (2011)Zhang, X.: Criteria for selecting the private-sector partner in public-private partnerships. J. Constr. Eng. Manage. 131(6), 631–644 (2005

Reliabilität - die Genauigkeit einer Messung (Version 1.1)

Die Reliabilität beschreibt die Genauigkeit einer Messung. In diesem Beitrag wird das Konzept Reliabilität definiert und es wird erläutert, warum die Reliabilität einer Messung relevant ist. Danach wird diskutiert, welche Modellannahmen getroffen werden müssen, um die Reliabilität einer Messung zu schätzen und es werden fünf Methoden zum Schätzen der Reliabilität vorgestellt: die Re-Test Korrelation, die Parallel-Test Korrelation, die Split-Half Korrelation, die interne Konsistenz und das Schätzen der Reliabilität mit Strukturgleichungsmodellen. Abschließend wird in knapper Form auf Gemeinsamkeiten und Unterschiede der klassischen Testtheorie und der Item-Response Theorie und deren Bedeutung für die Schätzung der Reliabilität eingegangen

Conceptual Framework for Managing Uncertainty in a Collaborative Agri-Food Supply Chain Context

[EN] Agri-food supply chains are subjected to many sources of uncertainty. If these uncertainties are not managed properly, they can have a negative impact on the agri-food supply chain (AFSC) performance, its customers, and the environment. In this sense, collaboration is proposed as a possible solution to reduce it. For that, a conceptual framework (CF) for managing uncertainty in a collaborative context is proposed. In this context, this paper seeks to answer the following research questions: What are the existing uncertainty sources in the AFSCs? Can collaboration be used to reduce the uncertainty of AFSCs? Which elements can integrate a CF for managing uncertainty in a collaborative AFSC? The CF proposal is applied to the weather source of uncertainty in order to show its applicability.The first author acknowledges the partial support of the Program of Formation of University Professors of the Spanish Ministry of Education, Culture, and Sport (FPU15/03595). The other authors acknowledge the partial support of the Project 691249, RUC-APS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems, funded by the EU under its funding scheme H2020-MSCA-RISE-2015.Esteso-Álvarez, A.; Alemany Díaz, MDM.; Ortiz Bas, Á. (2017). Conceptual Framework for Managing Uncertainty in a Collaborative Agri-Food Supply Chain Context. IFIP Advances in Information and Communication Technology. 506:715-724. https://doi.org/10.1007/978-3-319-65151-4_64S715724506Taylor, D.H., Fearne, A.: Towards a framework for improvement in the management of demand in agri-food supply chains. Supply Chain Manag. Int. J. 11, 379–384 (2006)Matopoulos, A., Vlachopoulou, M., Manthou, V., Manos, B.: A conceptual framework for supply chain collaboration: empirical evidence from the agri-food industry. Supply Chain Manag. Int. J. 12, 177–186 (2007)Ahumada, O., Villalobos, J.R.: Application of planning models in the agri-food supply chain: a review. Eur. J. Oper. Res. 196, 1–20 (2009)Tsolakis, N.K., Keramydas, C.A., Toka, A.K., Aidonis, D.A., Iakovou, E.T.: Agrifood supply chain management: a comprehensive hierarchical decision-making framework and a critical taxonomy. Biosyst. Eng. 120, 47–64 (2014)van der Vorst, J.G., Da Silva, C.A., Trienekens, J.H.: Agro-industrial supply chain management: Concepts and applications. FAO (2007)Borodin, V., Bourtembourg, J., Hnaien, F., Kabadie, N.: Handling uncertainty in agricultural supply chain management: a state of the art. Eur. J. Oper. Res. 254, 348–359 (2016)van der Vorst, J.G.A.J., Beulens, A.J.M.: Identifying sources of uncertainty to generate supply chain redesign strategies. Int. J. Phys. Distrib. Logist. Manag. 32, 409–430 (2000)Klosa, E.: A concept of models for supply chain speculative risk analysis and management. J. Econ. Manag. 12, 45–59 (2013)Samson, S., Reneke, J.A., Wiecek, M.M.: A review of different perspectices on uncertainty and risk and an alternative modeling paradigm. Reliab. Eng. Syst. Saf. 94, 558–567 (2009)Backus, G.B.C., Eidman, V.R., Dijkhuizen, A.A.: Farm decision making under risk and uncertainty. Neth. J. Agric. Sci. 45, 307–328 (1997)van der Vorst, J.G.: Effective food supply chains; Generating, modelling and evaluating supply chain scenarios. (2000)Amorim, P., Günther, H.O., Almada-Lobo, B.: Multi-objective integrated production and distribution planning of perishable products. Int. J. Prod. Econ. 138, 89–101 (2012)Amorim, P., Meyr, H., Almeder, C., Almada-Lobo, B.: Managing perishability in production-distribution planning: a discussion and review. Flex. Serv. Manuf. 25, 389–413 (2013)Costa, C., Antonucci, F., Pallottino, F., Aguzzi, J., Sarria, D., Menesatti, P.: A review on agri-food supply chain traceability by means of RFID technology. Food Bioprocess Technol. 6, 353–366 (2013)Pahl, J., Voss, S.: Integrating deterioration and lifetime constraints in production and supply chain planning: a survey. Eur. J. Oper. Res. 238, 654–674 (2014)Grillo, H., Alemany, M.M.E., Ortiz, A.: A review of Mathematical models for supporting the order promising process under Lack of Homogeneity in product and other sources of uncertainty. Comput. Ind. Eng. 91, 239–261 (2016)Zwietering, M.H., van’t Riet, K.: Modelling of the quality of food: optimization of a cooling chain. In: Management Studies and the Agri-business: Management of Agri-chains, Wageningen, The Netherlands, pp. 108–117 (1994)Akkerman, R., Farahani, P., Grunow, M.: Quality, safety and sustainability in food distribution: a review of quantitative operations management approaches and challenges. Spectrum 32, 863–904 (2010)Apaiah, R.K., Hendrix, E.M.T., Meerdink, G., Linnemann, A.R.: Qualitative methodology for efficient food chain design. Trends Food Sci. Technol. 16, 204–214 (2005)Lehmann, R.J., Reiche, R., Schiefer, G.: Future internet and the agri-food sector: State-of-the-art in literature and research. Comput. Electron. Agric. 89, 158–174 (2012)Kusumastuti, R.D., van Donk, D.P., Teunter, R.: Crop-related harvesting and processing planning: a review. Int. J. Prod. Econ. 174, 76–92 (2016)Dreyer, H.C., Strandhagen, J.O., Hvolby, H.H., Romsdal, A., Alfnes, E.: Supply chain strategies for speciality foods: a Norwegian case study. Prod. Plan. Control 27, 878–893 (2016)Baghalian, A., Rezapour, S., Farahani, R.Z.: Robust supply chain network design with service level against disruptions and demand uncertainties: a real-life case. Eur. J. Oper. Res. 227, 199–215 (2013)Aggarwal, S., Srivastava, M.K.: Towards a grounded view of collaboration in Indian agri-food supply chains: a qualitative investigation. Br. Food J. 115, 1085–1106 (2016)Teimoury, E., Nedaei, H., Ansari, S., Sabbaghi, M.: A multi-objective analysis for import quota policy making in a perishable fruit and vegetable supply chain: a system dynamics approach. Comput. Electron. Agric. 93, 37–45 (2013)Opara, L.U.: Traceability in agriculture and food supply chain: a review of basic concepts, technological implications, and future prospects. J. Food Agric. Environ. 1, 101–106 (2003)Kruize, J.W., Wolfert, S., Goense, D., Scholten, H., Beulens, A., Veenstra, T.: Integrating ICT applications for farm business collaboration processes using Fl Space. In: 2014 Annual SRII Global Conference, pp. 232–240. IEEE (2014)Oriade, C.A., Dillon, C.R.: Developments in biophysical and bioeconomic simulation of agricultural systems: a review. Agric. Econ. 17, 45–58 (1997)Camarinha-Matos, L.M., Afsarmanesh, H.: Collaborative networks: value creation in a knowledge society. In: Wang, Kesheng, Kovacs, G.L., Wozny, Michael, Fang, Minglun (eds.) PROLAMAT 2006. IIFIP, vol. 207, pp. 26–40. Springer, Boston, MA (2006). doi: 10.1007/0-387-34403-9_4Prima Dania, W.A., Xing, K., Amer, Y.: Collaboration and sustainable agri-food supply chain: a literature review. MATEC Web Conf. 58 (2016)Simatupang, T.M., Sridharan, R.: The collaborative index: a measure for supply chain collaboration. Int. J. Phys. Distrib. Logist. Manag. 35, 44–62 (2005)Fischer, C., Hartmann, M., Reynolds, N., Leat, P., Revoredo-Giha, C., Henchion, M., Albisu, L.M., Gracia, A.: Factors influencing contractual choice and sustainable relationships in European agri-food supply chains. Eur. Rev. Agric. Econ. 36, 541–569 (2009